Smart grids are an emerging engineering challenge. A discussion of the forms a smart grid can take can be found in A. Ipakchi and F. Albuyeh. Grid of the future. IEEE Power and Energy Magazine, 7(2):52-62, 2009. A smart grid may be viewed a system that itself unifies a number of sub-systems. A smart grid further merges multiple engineering technologies, such as electric power and control systems and telecommunication and information technology systems.
A smart grid is considered to intelligently integrate and optimise the functionalities of its components, to efficiently deliver sustainable, economic and secure electricity supplies. It may employ products and services together with monitoring, control, communications and self-healing technologies to offer a wide range of new services, such as improved grid connectivity, optimised consumer power supply or power reserve, improved customer services, control of the environmental impact and enhanced levels of reliability and security of supply.
Smart grids moreover differ from common (legacy) grids in that they interconnect smart grid components with a two-way communications network. This two-way connection enables energy suppliers and customers to exchange information, if required in an interactive and/or real-time manner. Information exchange of this nature can support features such as load shedding, consumption management, distributed energy storage (e.g. in electric cars) and distributed energy generation (e.g. from renewable resources).
Smart grids may further use an advanced metering infrastructure (AMI) and automated meter reading (AMR). The role of smart meters in an advanced metering infrastructure is pivotal. Smart meters, which are usually electrical meters but could also incorporate other metering devices, such as device metering gas, water and/or heat consumption, measure power consumption in much more detail than conventional meters. It is moreover anticipated that future smart meters will have the ability to communicate collected information to third parties, in particular the provider of a utility in question, i.e. the electricity provider.
The information security of smart grid data and advanced metering data is of paramount importance. Implementing and analysing smart grid security is a challenging task, especially when considering the scale of the potential damages that could be caused by attacks and by the compromising of advanced metering data. A classification of smart grid risks and vulnerabilities has been published by the National Institute of Standards and Technology (NIST) (see A. Lee and T. Brewer, “Smart grid Cyber Security Strategy and Requirements. Technical Report DRAFT” NISTIR 7628, The Cyber Security Coordination Task Group, Advanced Security Acceleration Project, National Institute of Technology, September 2009). In addition, a comprehensive specification of Advanced Metering Infrastructure security requirements has been published by OpenSG (“AMI System Security Requirements”, Technical Report AMI-SEC TF, OpenSG, December 2008).
The dangers of metering data to privacy have been widely discussed (see, for example, stories published by Smart Grid News.com (http://www.smartgridnews.com/artman/publish/industry/The_Dangers_of_Meter_Data_ Part_1.html) by the Washington Post (http://voices.washingtonpost.com/securityfix/2009/11/experts_smart_grid_poses_priva.html), another story published at http://information-security-resources.com/2009/11/15/fifteen-more-smart-grid-privacy-concerns and papers by Quinn (“Privacy and the New Energy Infrastructure”, available at http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1370731) and P. McDaniel, and S. McLaughlin, (“Security and Privacy Challenges in the Smart Grid”, IEEE Security & Privacy, 75-77, 2009). Quinn argues that in the future advanced metering Infrastructure will provide a window into the activities within homes, exposing private activities to anyone with access to electricity usage information. As more and more detailed data about home energy usage is pouring into utilities, the resolution and quality of information that can be gleaned from that raw data is increasing. Quinn moreover argues that modern analytical techniques can, based on an electricity usage profile, identify the use of individual appliances within homes, and will in the foreseeable future be able to pinpoint exactly who, how, and when someone has operated these home appliances. For example, it may be possible to recognise when a resident showers, watches TV, and goes to bed in the night. The privacy threat smart metering imposes hereby goes beyond the threat of private information being exposed to another individual. The privacy threat associated with smart metering is that smart metering can allow the collating and analysing of the collected personal data on an industrial scale.
Despite the threats to users' privacy, it is envisaged that more detailed power usage information will be required in the future to:                To enable demand response functionality and sustainable load management.        To accommodate variable input from renewable resources.        To drive consumer actions through awareness and social pressure with demand-side management.        
The granularity of the data acquired by smart meters may vary widely. The Solarwave Smart Sub Meter, for example (see http://www.solarwave.ie/HowItWorks.htm) meters power consumption at fifteen-minute intervals as a default but is capable of taking data every minute.
The information that can be gleaned from the processing of power profiles that can be generated by smart meters and subsequently provided to utility companies can currently be demonstrated with the use of non-intrusive appliance load monitors (NALM) (see, for example, C. Laughman et al., “Advanced Nonintrusive Monitoring of Electric Loads, IEEE Power and Energy, 56, March/April 2003). Non-intrusive appliance load monitors can be used for constructing appliance models. Appliance models can be separated into two basic types: on/off models, and finite state machine models. Appliance models can then be used to track appliance behaviour, as illustrated, for example, by G. W. Hart in “Nonintrusive Appliance Load Monitoring”, 80 Proceedings of the IEEE 1870, 1871-72, Dec. 1992.
There is moreover a rich and ongoing line of research in the construction and upkeep of appliance libraries and detection algorithms, as illustrated, for example, by H. Y. Lam & W. K. Lee in “A Novel Method to Construct Taxonomy of Electrical Appliances Based on Load Signatures”, 53 IEEE Transactions On Consumer Electronics 653, 2007. By way of example, FIGS. 1 and 2 show two signature load profiles (with different time granularity) for a house, from which a large amount of personal information can be extracted, as indicated.
Even when household power profiles are aggregated, researchers have shown (with the use of artificial neural networks) that they can pinpoint the use of washing machines, dishwashers and water heaters with accuracy rates of over 90% from within the noise of the aggregated load information (see, for example, A. Prudenzi, “A Neuron Nets Based Procedure for Identifying Domestic Appliances Pattern-of-Use from Energy Recordings at Meter Panel”, IEEE Power Engineering Society Winter Meeting 941, 942 col. 1, 2002).
The full extent of privacy concerns is not yet fully understood. A good list of privacy threats is given by Rebecca Herold (from NIST) and can be found at http://www.privacyguidance.com/files/SmartGrid_PrivacyHeroldOct2009.pdf. These considerations have been neatly summarised in a report from a NIST expert: “The major benefit provided by the Smart Grid, i.e. the ability to get richer data to and from customer meters and other electric devices, is also its Achilles' heel from a privacy viewpoint.”
Current art for protecting smart metering data privacy is focusing on policy formulation and enforcement in the domains that will be managing this data (e.g. in the utility provider domain).